当前位置: X-MOL 学术Br. J. Math. Stat. Psychol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Asymptotic bias of normal-distribution-based maximum likelihood estimates of moderation effects with data missing at random.
British Journal of Mathematical and Statistical Psychology ( IF 1.5 ) Pub Date : 2018-11-25 , DOI: 10.1111/bmsp.12151
Qian Zhang 1 , Ke-Hai Yuan 2 , Lijuan Wang 2
Affiliation  

Moderation analysis is useful for addressing interesting research questions in social sciences and behavioural research. In practice, moderated multiple regression (MMR) models have been most widely used. However, missing data pose a challenge, mainly because the interaction term is a product of two or more variables and thus is a non‐linear function of the involved variables. Normal‐distribution‐based maximum likelihood (NML) has been proposed and applied for estimating MMR models with incomplete data. When data are missing completely at random, moderation effect estimates are consistent. However, simulation results have found that when data in the predictor are missing at random (MAR), NML can yield inaccurate estimates of moderation effects when the moderation effects are non‐null. Simulation studies are subject to the limitation of confounding systematic bias with sampling errors. Thus, the purpose of this paper is to analytically derive asymptotic bias of NML estimates of moderation effects with MAR data. Results show that when the moderation effect is zero, there is no asymptotic bias in moderation effect estimates with either normal or non‐normal data. When the moderation effect is non‐zero, however, asymptotic bias may exist and is determined by factors such as the moderation effect size, missing‐data proportion, and type of missingness dependence. Our analytical results suggest that researchers should apply NML to MMR models with caution when missing data exist. Suggestions are given regarding moderation analysis with missing data.

中文翻译:

基于正态分布的缓和效应的最大似然估计的渐近偏差,数据随机丢失。

适度分析对于解决社会科学和行为研究中有趣的研究问题很有用。在实践中,调节多元回归(MMR)模型已得到最广泛的使用。但是,缺少数据带来了挑战,主要是因为交互项是两个或多个变量的乘积,因此是所涉及变量的非线性函数。已经提出了基于正态分布的最大似然(NML),并将其应用于估计数据不完整的MMR模型。当数据完全随机丢失时,调节效果估计将保持一致。但是,仿真结果发现,当预测变量中的数据随机丢失(MAR)时,如果调节效果为非零,则NML会产生不正确的调节效果估计。模拟研究受到将系统偏差与采样误差混淆的局限性。因此,本文的目的是通过分析得出MAR数据对NML缓和效应估计的渐近偏差。结果表明,当适度效应为零时,无论是正常数据还是非正常数据,在缓和效应估计中都没有渐近偏差。但是,当适度效应为非零值时,可能会出现渐近偏差,并由诸如适度效应量,缺失数据比例和缺失依赖类型等因素决定。我们的分析结果表明,当缺少数据时,研究人员应谨慎地将NML应用于MMR模型。给出了有关缺少数据的适度分析的建议。本文的目的是通过MAR数据分析得出NML的缓和效应估计的渐近偏差。结果表明,当适度效应为零时,无论是正常数据还是非正常数据,在缓和效应估计中都没有渐近偏差。但是,当适度效应为非零值时,可能会出现渐近偏差,并由诸如适度效应量,缺失数据比例和缺失依赖类型等因素决定。我们的分析结果表明,当缺少数据时,研究人员应谨慎地将NML应用于MMR模型。给出了有关缺少数据的适度分析的建议。本文的目的是通过MAR数据分析得出NML估计的缓和效应的渐近偏差。结果表明,当适度效应为零时,无论是正常数据还是非正常数据,在缓和效应估计中都没有渐近偏差。但是,当适度效应为非零值时,可能会出现渐近偏差,并由诸如适度效应量,缺失数据比例和缺失依赖类型等因素决定。我们的分析结果表明,当缺少数据时,研究人员应谨慎地将NML应用于MMR模型。给出了有关缺少数据的适度分析的建议。正常数据或非正常数据的调节效果估计值都没有渐近偏差。但是,当适度效应为非零值时,可能会出现渐近偏差,并由诸如适度效应量,缺失数据比例和缺失依赖类型等因素决定。我们的分析结果表明,当缺少数据时,研究人员应谨慎地将NML应用于MMR模型。给出了有关缺少数据的适度分析的建议。正常数据或非正常数据的调节效果估计值都没有渐近偏差。但是,当适度效应为非零值时,可能会出现渐近偏差,并由诸如适度效应量,缺失数据比例和缺失依赖类型等因素决定。我们的分析结果表明,当缺少数据时,研究人员应谨慎地将NML应用于MMR模型。给出了有关缺少数据的适度分析的建议。
更新日期:2018-11-25
down
wechat
bug